Load libraries

library(tidyverse) # data manipulation
library(ggpubr) # producing data exploratory plots
library(modelsummary) # descriptive data 
library(glmmTMB) # running generalised mixed models 
library(DHARMa) # model diagnostics 
library(performance) # model diagnostics  
library(ggeffects) # partial effect plots 
library(car) # running Anova on model 
library(emmeans) # post-hoc analysis

Import data

df_adults <- read_csv("import_data/resp_results_adults.csv")
df_jresp <- read_csv("import_data/resp_results_juveniles.csv")

Data manipulation

Adults

df_adults_cleaned <- df_adults |> 
  mutate(FISH_ID = factor(FISH_ID), 
         Sex = factor(Sex), 
         Population = factor(Population), 
         Tank = factor(Tank), 
         Chamber = factor(Chamber), 
         System =factor(System), 
         Temperature =factor(Temperature), 
         True_resting=factor(True_resting)) 

df_males <- df_adults_cleaned |> 
  filter(Sex == "M")
df_females <- df_adults_cleaned |> 
  filter(Sex == "F")

df_adults_cleaned2 <- df_males |> 
  full_join(select(df_females, c("Tank","Temperature","Mass","Resting","Max","AAS","FISH_ID","Sex")), by="Tank") |> 
  mutate(Temperature.x = coalesce(Temperature.x, Temperature.y), 
         FISH_ID.x = coalesce(FISH_ID.x, FISH_ID.y),
         Sex.x = coalesce(Sex.x, Sex.y))

Juveniles

df_jresp$Population <-  fct_collapse(df_jresp$Population, 
                                      `Vlassof cay`= c("Vlassof reef", "Vlassof", "Vlassof Cay", "Vlassof cay"), 
                                      `Arlington reef` = c("Arlington reef","Arlginton reef")) 

#df_jresp$Female <-  fct_collapse(df_jresp$Female, 
                                  #`CARL359`= c("CARL359", "CARL59")) 


df_jresp2 <-  df_jresp |> 
  unite("F0", c("Male","Female"), sep="_", remove=FALSE) |>
  mutate(across(1:7, factor), 
         Temperature = factor(Temperature), 
         True_resting = factor(True_resting)) 

#df_jresp2_rest <- df_jresp2 |> 
  #filter(True_resting == "Y")

Merging dataframes

temp1a <- df_jresp2 |> 
  mutate(FISH_ID.x = Male)
temp1b <- df_jresp2 |> 
  mutate(FISH_ID.y = Female)
temp2a <- temp1a |> 
  left_join(select(df_adults_cleaned2, c("FISH_ID.x",
                                          "Sex.x",
                                          "Resting.x", 
                                          "Max.x", 
                                          "AAS.x", 
                                          "Mass.x")), 
                    by="FISH_ID.x")
temp2b <- temp1b |> 
  left_join(select(df_adults_cleaned2, c("FISH_ID.y",
                            "Sex.y",
                            "Resting.y", 
                            "Max.y", 
                            "AAS.y", 
                            "Mass.y")), 
                   by="FISH_ID.y") 
df_merged <- temp2a |> 
  left_join(select(temp2b, c("Clutch","Replicate", 
                             "FISH_ID.y",
                             "Resting.y", 
                             "Max.y", 
                             "AAS.y", 
                             "Mass.y")), 
            by=c("Clutch","Replicate"))
df <- df_merged |> 
  mutate(Resting_MALE =Resting.x, 
         Max_MALE =Max.x, 
         AAS_MALE =AAS.x, 
         Mass_MALE =Mass.x, 
         FISH_ID.y =FISH_ID.x,#makes more sense for males to be .y instead of .x
         FISH_ID.x =FISH_ID.x, 
         Resting_FEMALE =Resting.y, 
         Max_FEMALE =Max.y, 
         AAS_FEMALE =AAS.y, 
         Mass_FEMALE =Mass.y) |>  
  mutate(AAS_MALE = AAS_MALE/Mass_MALE, 
         AAS_FEMALE =AAS_FEMALE/Mass_FEMALE) |>
  mutate(AAS_MID =(AAS_MALE+AAS_FEMALE)/2) |> # easier to do it again
  filter(True_resting == "Y") |> # easier to do it again
  mutate(AAS_MID =coalesce(AAS_MID, AAS_MALE)) |> 
  mutate(AAS_MID =coalesce(AAS_MID, AAS_FEMALE)) |> 
  drop_na(AAS) |> 
  group_by(Clutch) |> 
  mutate(MEDIAN_AAS =median(AAS_kg_wet)) |> 
  ungroup() |> 
  select(-c(Replicate, Chamber, System, Volume, Date_tested, Swim, Mass, Dry_mass, 18:26)) |> 
  distinct() |> 
  drop_na(AAS_MID)

Exploratory analysis

Offspring-Male

plot <- ggplot(df, aes(x=AAS_MALE, y=MEDIAN_AAS, color=Temperature)) + 
  stat_smooth(method = "lm") +
  #geom_point(alpha=0.1) + 
  ggtitle("Offspring-male relationship") +
  xlab("AAS (offspring)") + 
  ylab("AAS (parental-male)") +
  theme_classic() + 
  theme(legend.position = 'right') 
plot

Offspring-Midpoint

plot <- ggplot(df, aes(x=AAS_MID, y=MEDIAN_AAS, color=Temperature)) + 
  stat_smooth(method = "lm") +
  #geom_point(alpha=0.1) + 
  ggtitle("Offspring-midpoint relationship") +
  xlab("AAS (offspring)") + ylab("AAS (parental-midpoint)") +
  theme_classic() + 
  theme(legend.position = 'right') 
plot

Descriptive statistics

Juveniles - overview

Overview

tinytable_2qfm3zxhwzj4cl4t1jvu
Population 27 28.5 30
Arlington reef 9 6 3
Pretty patches 4 3 5
Sudbury reef 4 2 2
Vlassof cay 4 0 4 
datasummary(Factor(F0) ~ Factor(Temperature), 
            data = df, 
            fmt = "%.0f")
tinytable_7owdijm4df5kpp7twzu5
F0 27 28.5 30
CARL217_CARL226 0 1 0
CARL218_CARL222 0 0 2
CARL230_CARL235 2 0 0
CARL233_CARL215 0 0 0
CARL237_CARL219 2 0 0
CARL241_CARL239 2 0 0
CARL249_CARL360 0 0 1
CARL335_CARL359 0 2 0
CARL338_CARL345 0 1 0
CARL344_CARL370 0 0 0
CARL354_CARL355 3 0 0
CARL360_CARL249 0 0 0
CARL367_CARL363 0 1 0
CARL369_CARL349 0 1 0
CPRE189_CPRE202 0 0 2
CPRE372_CPRE209 1 0 0
CPRE372_CPRE370 1 0 0
CPRE375_CPRE377 2 0 0
CPRE391_CPRE390 0 0 1
CPRE447_CPRE452 0 0 2
CPRE453_CPRE459 0 1 0
CPRE521_CPRE524 0 1 0
CPRE550_CPRE533 0 1 0
CSUD002_CSUD213 0 1 0
CSUD009_CSUD212 2 0 0
CSUD013_CSUD017 2 0 0
CSUD016_CSUD078 0 1 0
CSUD312_CSUD304 0 0 2
CVLA049_CVLA098 0 0 0
CVLA089_CVLA059 0 0 1
CVLA102_CVLA466 1 0 0
CVLA106_CVLA091 0 0 2
CVLA468_CVLA477 2 0 0
CVLA486_CVLA463 1 0 0
CVLA498_CVLA493 0 0 1

Juveniles

Absolute aerobic scope

tinytable_kejjdeb40dhsyqvf7a44
Temperature NUnique mean median min max sd Histogram
27 21 584.54 578.01 421.02 793.67 91.11 ▁▃▄▄▇▄▃▃
28.5 11 699.55 711.16 514.83 826.07 101.40 ▃▃▃▃▇▃▇▇
30 14 767.46 718.59 597.88 1022.17 147.90 ▇▇▅▂▂▂▂▅

Adults - overview

Overview

datasummary(Factor(Population) ~ Factor(Temperature), 
            data = df_adults_cleaned, 
            fmt = "%.0f")
tinytable_gqlap0smsdutk170g3qc
Population 27 28.5 30
Arlington reef 8 7 4
Pretty patches 4 6 4
Sudbury reef 4 3 2
Vlassof cay 6 2 5 
datasummary(Factor(Population) ~ Factor(Temperature)*Factor(Sex), 
            data = df_adults_cleaned, 
            fmt = "%.0f")
tinytable_qeun9otg8ie6w2mqmq16
27 28.5 30
Population F M F M F M
Arlington reef 4 4 2 5 2 2
Pretty patches 2 2 3 3 3 1
Sudbury reef 2 2 1 2 1 1
Vlassof cay 3 3 1 1 3 2

Pairs

datasummary(Factor(Population)*Factor(Temperature.x) ~ AAS.x*(NUnique), 
            data = df_adults_cleaned2, 
            fmt = "%.0f")
tinytable_027aau9w0uizx2na97jf
Population Temperature.x NUnique
Arlington reef 27 4
28.5 5
30 2
Pretty patches 27 2
28.5 3
30 1
Sudbury reef 27 2
28.5 2
30 1
Vlassof cay 27 3
28.5 1
30 2

Adults

Absolute aerobic scope

tinytable_t57nf2ke9sk3gwvch3eq
Temperature NUnique mean median min max sd Histogram
27 22 10.29 10.26 3.85 16.28 3.14 ▃▁▄▇▃▆▃▃▁
28.5 18 10.59 9.66 6.11 20.44 3.66 ▅▅▇▇▃▂▂
30 15 9.19 9.16 4.36 12.77 2.91 ▃▂▅▂▂▂▃▇

Fit Models [random factors]

model1 <- glmmTMB(MEDIAN_AAS ~ 1, 
                  family=gaussian(),
                  data = df) 

model2 <- glmmTMB(MEDIAN_AAS ~ (1|Population), 
                  family=gaussian(),
                  data = df)
Model selection
AIC(model1, model2, k=2)
BIC(model1, model2)

Model1 performs the best therefore only Clutch will be used as a random factor in future models

Relationships

Offspring-Male

Fit model [fixed factors]

After figuring out which random factors will be incorporated into the model we will start to examine out fixed factors. Some fixed factors such as AAS_(FE)MALE and TEMPERATURE will be essential to answering questions we have around heritability. Another factor that will be included is Dry_mass - which it should be pointed out in this experiment refers to the mass of fish after they were blotted dry with paper towel rather than completely dried out. Larger fish consume more oxygen, therefore, we need to account for this known relationship within our model. Out model will look something like this:

MEDIAN_AAS ~ scale(AAS_MALE)*Temprature

If we had alternative hypotheses to test would would do so at this stage. But in this instance the experiment was designed to answer a specific question via limiting potential covariates.

model1.1 <- glmmTMB(MEDIAN_AAS ~ scale(AAS_MALE)*Temperature, 
                    family=gaussian(), 
                    data=df)

Great now lets check how out model performed via model validation techniques

Model validation

To check out model performance we will be using two different packages that perform model diagnositics. The packages used here are just examples, there are other packages out there that can provide the same function.

DHARMa

model1.1 |> 
  simulateResiduals(plot=TRUE) 

## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help. 
##  
## Scaled residual values: 0.192 0.448 0.752 0.212 0.108 0.04 0.216 0.44 0.132 0.052 0.284 0.768 0.568 0.108 0.404 0.62 0.572 0.128 0.984 0.768 ...
model1.1 |> 
  testResiduals(plot=TRUE)

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.091619, p-value = 0.8724
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0291, p-value = 0.84
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 42, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.00000000 0.08408385
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                                      0
## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.091619, p-value = 0.8724
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0291, p-value = 0.84
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 42, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.00000000 0.08408385
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                                      0

performance

model1.1 |> check_model(detrend=FALSE)
## `check_outliers()` does not yet support models of class `glmmTMB`.

Partial effect plots

model1.1 |> ggemmeans(~AAS_MALE|Temperature) |> 
  plot(add.data =FALSE)

Model investigations

summary

model1.1 |> summary()
##  Family: gaussian  ( identity )
## Formula:          MEDIAN_AAS ~ scale(AAS_MALE) * Temperature
## Data: df
## 
##      AIC      BIC   logLik deviance df.resid 
##    525.5    537.7   -255.7    511.5       35 
## 
## 
## Dispersion estimate for gaussian family (sigma^2): 1.14e+04 
## 
## Conditional model:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                       589.17      23.92  24.632  < 2e-16 ***
## scale(AAS_MALE)                    24.77      29.21   0.848  0.39655    
## Temperature28.5                   116.23      41.03   2.833  0.00461 ** 
## Temperature30                     213.49      41.45   5.151 2.59e-07 ***
## scale(AAS_MALE):Temperature28.5   -46.44      43.52  -1.067  0.28593    
## scale(AAS_MALE):Temperature30     -36.91      40.11  -0.920  0.35735    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA

model1.1 |> Anova()

confint

model1.1 |> confint()
##                                      2.5 %    97.5 %  Estimate
## (Intercept)                      542.28902 636.05054 589.16978
## scale(AAS_MALE)                  -32.48823  82.02028  24.76602
## Temperature28.5                   35.81707 196.65283 116.23495
## Temperature30                    132.25704 294.72983 213.49344
## scale(AAS_MALE):Temperature28.5 -131.73997  38.85823 -46.44087
## scale(AAS_MALE):Temperature30   -115.51948  41.69145 -36.91402

r-squared

model1.1 |> r2_nakagawa()
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
## 
##   Conditional R2: NA
##      Marginal R2: 0.429

Pairwise comparisons

emmeans [Temperature]

model1.1 |> emmeans(pairwise ~ Temperature, type="response") |> 
  summary(by=NULL, adjust="sidak", infer=TRUE)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  Temperature emmean   SE df lower.CL upper.CL t.ratio p.value
##  27             589 23.9 35      529      649  24.632  <.0001
##  28.5           705 33.3 35      622      789  21.160  <.0001
##  30             803 33.8 35      718      888  23.713  <.0001
## 
## Confidence level used: 0.95 
## Conf-level adjustment: sidak method for 3 estimates 
## P value adjustment: sidak method for 3 tests 
## 
## $contrasts
##  contrast                        estimate   SE df lower.CL upper.CL t.ratio
##  Temperature27 - Temperature28.5   -116.2 41.0 35     -219    -13.4  -2.833
##  Temperature27 - Temperature30     -213.5 41.4 35     -317   -109.6  -5.151
##  Temperature28.5 - Temperature30    -97.3 47.5 35     -216     21.9  -2.047
##  p.value
##   0.0226
##   <.0001
##   0.1378
## 
## Confidence level used: 0.95 
## Conf-level adjustment: sidak method for 3 estimates 
## P value adjustment: sidak method for 3 tests

Summary figure

om.aas <- emmeans(model1.1, ~AAS_MALE*Temperature, 
                   at =list(AAS_MALE=seq(from=100, to =400, by=5)))

om.aas.df <- as.data.frame(om.aas)

om.aas.obs <- drop_na(df, AAS_MALE, MEDIAN_AAS) |> 
  mutate(Pred =predict(model1.1, re.form =NA, type='response'), 
         Resid =residuals(model1.1, type ="response"), 
         Fit =Pred + Resid) 

om.aas.obs.summarize <- om.aas.obs |> 
  group_by(Clutch, Temperature) |> 
  summarise(mean.aas =mean(Fit, na.rm=TRUE),
            mean.aas_male =mean(AAS_MALE, na.rm=TRUE),
            sd.aas =sd(Fit, na.rm =TRUE), 
            n.aas = n()) |> 
  mutate(se.aas = sd.aas / sqrt(n.aas), 
         lower.ci.aas =mean.aas - qt(1 - (0.05/2), n.aas -1) * se.aas, 
         upper.ci.aas =mean.aas + qt(1 - (0.05/2), n.aas - 1) * se.aas)|>
  ungroup()
## `summarise()` has grouped output by 'Clutch'. You can override using the
## `.groups` argument.
## Warning: There were 84 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `lower.ci.aas = mean.aas - qt(1 - (0.05/2), n.aas - 1) *
##   se.aas`.
## ℹ In group 1: `Clutch = 38`.
## Caused by warning in `qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 83 remaining warnings.
ggplot(data =om.aas.df, aes(y=emmean, x=AAS_MALE)) + 
  stat_smooth(aes(color=Temperature), 
              method = "lm") + 
  geom_pointrange(data = om.aas.obs.summarize, aes(y =mean.aas, x=mean.aas_male, 
                                                    ymin =lower.ci.aas, 
                                                    ymax =upper.ci.aas, color = Temperature), 
                  alpha =0.2) + 
  facet_wrap(~Temperature) +
  theme_classic() + 
  theme(legend.position ="bottom")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 21 rows containing missing values or values outside the scale range
## (`geom_segment()`).
## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_segment()`).
## Warning: Removed 10 rows containing missing values or values outside the scale range
## (`geom_segment()`).

Offspring-midpoint

Fit model [fixed factors]

mid_model1.1 <- glmmTMB(MEDIAN_AAS ~ scale(AAS_MID)*Temperature, 
                    family=gaussian(), 
                    data=df)

Great now lets check how out model performed via model validation techniques

Model validation

To check out model performance we will be using two different packages that perform model diagnositics. The packages used here are just examples, there are other packages out there that can provide the same function.

DHARMa

mid_model1.1 |> 
  simulateResiduals(plot=TRUE) 

## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help. 
##  
## Scaled residual values: 0.224 0.464 0.744 0.296 0.12 0.144 0.036 0.16 0.024 0.46 0.208 0.04 0.272 0.808 0.708 0.188 0.368 0.588 0.576 0.592 ...
mid_model1.1 |> 
  testResiduals(plot=TRUE)

## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.080348, p-value = 0.9278
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0232, p-value = 0.856
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 46, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.00000000 0.07706183
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                                      0
## $uniformity
## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  simulationOutput$scaledResiduals
## D = 0.080348, p-value = 0.9278
## alternative hypothesis: two-sided
## 
## 
## $dispersion
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0232, p-value = 0.856
## alternative hypothesis: two.sided
## 
## 
## $outliers
## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput
## outliers at both margin(s) = 0, observations = 46, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.00000000 0.07706183
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                                      0

performance

mid_model1.1 |> check_model(detrend=FALSE)
## `check_outliers()` does not yet support models of class `glmmTMB`.

Partial effect plots

mid_model1.1 |> ggemmeans(~AAS_MID|Temperature) |> 
  plot(add.data =FALSE)

Model investigations

summary

mid_model1.1 |> summary()
##  Family: gaussian  ( identity )
## Formula:          MEDIAN_AAS ~ scale(AAS_MID) * Temperature
## Data: df
## 
##      AIC      BIC   logLik deviance df.resid 
##    575.3    588.1   -280.6    561.3       39 
## 
## 
## Dispersion estimate for gaussian family (sigma^2): 1.17e+04 
## 
## Conditional model:
##                                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      583.47      23.62  24.703  < 2e-16 ***
## scale(AAS_MID)                    15.77      23.73   0.665  0.50621    
## Temperature28.5                  116.20      40.58   2.864  0.00419 ** 
## Temperature30                    194.78      38.82   5.017 5.24e-07 ***
## scale(AAS_MID):Temperature28.5   -16.40      35.86  -0.457  0.64750    
## scale(AAS_MID):Temperature30      25.98      48.01   0.541  0.58848    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA

mid_model1.1 |> Anova()

confint

mid_model1.1 |> confint()
##                                    2.5 %    97.5 %  Estimate
## (Intercept)                    537.18003 629.76610 583.47307
## scale(AAS_MID)                 -30.73158  62.27672  15.77257
## Temperature28.5                 36.66997 195.73382 116.20190
## Temperature30                  118.68956 270.86554 194.77755
## scale(AAS_MID):Temperature28.5 -86.68314  53.88895 -16.39710
## scale(AAS_MID):Temperature30   -68.12866 120.08465  25.97799

r-squared

mid_model1.1 |> r2_nakagawa()
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
## 
##   Conditional R2: NA
##      Marginal R2: 0.373

Summary figure

om.aas <- emmeans(mid_model1.1, ~AAS_MID*Temperature, 
                   at =list(AAS_MID =seq(from=100, to =450, by=5)))

om.aas.df <- as.data.frame(om.aas)

om.aas.obs <- drop_na(df, AAS_MID, MEDIAN_AAS) |> 
  mutate(Pred =predict(mid_model1.1, re.form =NA, type='response'), 
         Resid =residuals(mid_model1.1, type ="response"), 
         Fit =Pred + Resid) 

om.aas.obs.summarize <- om.aas.obs |> 
  group_by(Clutch, Temperature) |> 
  summarise(mean.aas =mean(Fit, na.rm=TRUE),
            mean.aas_female =mean(AAS_MID, na.rm=TRUE),
            sd.aas =sd(Fit, na.rm =TRUE), 
            n.aas = n()) |> 
  mutate(se.aas = sd.aas / sqrt(n.aas), 
         lower.ci.aas =mean.aas - qt(1 - (0.05/2), n.aas -1) * se.aas, 
         upper.ci.aas =mean.aas + qt(1 - (0.05/2), n.aas - 1) * se.aas)|>
  ungroup()
## `summarise()` has grouped output by 'Clutch'. You can override using the
## `.groups` argument.
## Warning: There were 92 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `lower.ci.aas = mean.aas - qt(1 - (0.05/2), n.aas - 1) *
##   se.aas`.
## ℹ In group 1: `Clutch = 38`.
## Caused by warning in `qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 91 remaining warnings.
ggplot(data =om.aas.df, aes(y=emmean, x=AAS_MID)) + 
  stat_smooth(aes(color=Temperature), 
              method = "lm") + 
  geom_pointrange(data = om.aas.obs.summarize, aes(y =mean.aas, x=mean.aas_female, 
                                                    ymin =lower.ci.aas, 
                                                    ymax =upper.ci.aas, color = Temperature), 
                  alpha =0.2) + 
  facet_wrap(~Temperature) +
  theme_classic() + 
  theme(legend.position ="bottom")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 21 rows containing missing values or values outside the scale range
## (`geom_segment()`).
## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_segment()`).
## Warning: Removed 14 rows containing missing values or values outside the scale range
## (`geom_segment()`).